Digital Twin Design of a Turbulence Inhibitor in a Tundish Based on the Production Cluster Mining Algorithm

Abstract

The lack of a direct and linear relation between inclusion removal from tundishes and the design of their turbulence inhibitors is a difficult challenge. In contrast to the traditional method of optimizing flow control devices based on the residence time distribution curve, this study used the inclusion/flow field database production clustering mining algorithm to conduct step-by-step data mining on the tundish flow field; to produce relevant facts of the flow field characteristics in the inclusion aggregation zone; and to extract the data mining results from the fact database to screen a digital twin algorithm that forecasts the inclusion aggregation area in a tundish to optimize the flow control device. The results showed that the inclusion aggregation area in the tundish impact zone is above the turbulence inhibitor and that the inclusion aggregation area outside the tundish impact zone is at the vortex center of the flow field. According to the mining results, a pseudo-code for screening the inclusion aggregation area was developed, and the turbulence inhibitor was optimized with the help of the digital convergence of the digital and physical models. Finally, in a tundish, the inclusion removal rate in molten steel was increased by 14.4%. The turbulence inhibitor designed by the digital twin method is currently being used in a Chinese steel mill.

Keywords

• cleanliness of liquid steel
• distributed data mining
• inclusion aggregation area
• auxiliary optimization